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CA prompt-enhancer

Улучшает и структурирует короткие неструктурированные промпты и постановки задач, превращая их в подробные императивные ТЗ с пронумерованными шагами анализа, явными граничными случаями и описанием формата ожидаемого вывода. Сохраняет все термины и условия исходника, не добавляет новых требований. Используй когда пользователь просит: улучшить промпт, проработать постановку, расширить описание задачи, структурировать ТЗ, оформить заметку в подробную инструкцию, сделать промпт точнее или подробнее, подготовить ТЗ из черновика, причесать задание, преобразовать короткую заметку в понятное ТЗ. Принимает текст в аргументе команды или путь к .md файлу.

Desko77/claude-code-skills-1c Claude Code author: Desko77 MIT 2 files body ≈ 2 821 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Улучшает и структурирует короткие неструктурированные промпты и постановки задач, превращая их в подробные императивные ТЗ с пронумерованными шагами анализа…

As a process A 80/100 · Runs to the end — weak spots: inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
A
80/100
Runs to the end
Inputs and preconditions w 11
0
Result and completion w 14
60
When it triggers w 12
70
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 80/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 48 steps
  • 100Failures and branches. 7 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2821 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 11 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +3Description length 652: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 48 items
  • +2Bilingual instructions (RU + EN)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.